Virtual interactive conversational agents, which are commonly referred to as chatbots, are used with increasing frequency, for example, to interact with customers. Available frameworks for constructing conversational agents make the creation of conversational agents easy, increasing the rate of creation of conversational agents and allowing the creation of conversational agents by programmers that are not experts in artificial intelligence.
Some conversational agents communicate or interact using either text commands or voice commands. Other conversational agents utilize the content of a natural language conversation. However, virtual conversational agents are not as proficient as human beings in understanding a natural language conversation. Therefore, these virtual conversational agents have an associated level of accuracy and make mistakes based on a misunderstanding of the natural language conversation or an improper identification of an intended action contained in the natural language conversation. Given the current level of accuracy associated with virtual conversational agents, actions that the virtual conversational agents are allowed to take are limited to relatively simple or innocuous tasks, e.g., turning lights on and off or adjusting the volume of music coming out of a speaker. Consequences or repercussions associated with these simple tasks are low and easily reversed or rectified.
With increased sophistication and quality, the level of understanding and accuracy of natural language conversation by the conversational agents increases. This increase in accuracy will prompt the use of conversational agents to execute more advanced actions. These more advanced actions can be associated with tasks having a higher risk level, e.g., transferring money or charging a credit card. Improper identification of these tasks from a natural language conversation and the subsequent improper execution of these higher risk tasks can result in consequences that are not easily reversed and that have monetary consequences. As the level of risks associated with tasks increases, the associated level of adverse consequences also increases.
The increased risk levels and associated adverse consequences motivate designers and creators of conversational agents to construct these conversational agents to confirm the actions that the conversational agent interprets from the natural language conversation. However, the designers and creators may not think to include these confirmations or may not adequately or completely identify inputs and actions that could trigger high risk actions having associated serious consequences.